{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Matplotlib图鉴——基础箱线图\n",
    "\n",
    "## 公众号：可视化图鉴"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.3.3\n",
      "1.2.0\n",
      "1.19.4\n"
     ]
    }
   ],
   "source": [
    "import matplotlib\n",
    "print(matplotlib.__version__) #查看Matplotlib版本\n",
    "import pandas as pd\n",
    "print(pd.__version__) #查看pandas版本\n",
    "import numpy as np\n",
    "print(np.__version__) #查看numpy版本\n",
    "import matplotlib.pyplot as plt \n",
    "plt.rcParams['font.sans-serif'] = ['STHeiti'] #设置中文"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "注意，代码在以下环境全部通过测试:\n",
    "- Python 3.7.1\n",
    "- Matplotlib == 3.3.3\n",
    "- pandas == 1.2.0\n",
    "- numpy == 1.19.4\n",
    "\n",
    "因版本不同，可能会有部分语法差异，如有报错，请先检查拼写及版本是否一致！"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 进阶箱线图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.patches import Polygon\n",
    "\n",
    "# via https://matplotlib.org/gallery/statistics/boxplot_demo.html?highlight=boxplot\n",
    "random_dists = ['Normal(1, 2)', 'Lognormal(1, 3)', 'Exp(3)', 'Gumbel(6, 5)',\n",
    "                'Triangular(3, 8, 11)']\n",
    "N = 1793\n",
    "\n",
    "norm = np.random.normal(1, 2, N)\n",
    "logn = np.random.lognormal(1, 3, N)\n",
    "expo = np.random.exponential(3, N)\n",
    "gumb = np.random.gumbel(6, 5, N)\n",
    "tria = np.random.triangular(3, 8, 11, N)\n",
    "\n",
    "bootstrap_indices = np.random.randint(0, N, N)\n",
    "data = [\n",
    "    norm, norm[bootstrap_indices],\n",
    "    logn, logn[bootstrap_indices],\n",
    "    expo, expo[bootstrap_indices],\n",
    "    gumb, gumb[bootstrap_indices],\n",
    "    tria, tria[bootstrap_indices],\n",
    "]\n",
    "\n",
    "fig, ax1 = plt.subplots(figsize=(10, 9))\n",
    "fig.subplots_adjust(left=0.075, right=0.95, top=0.9, bottom=0.25)\n",
    "\n",
    "bp = ax1.boxplot(data, notch=0, sym='o', vert=1, whis=1.5)\n",
    "plt.setp(bp['boxes'], color='black')\n",
    "plt.setp(bp['whiskers'], color='black')\n",
    "plt.setp(bp['fliers'], color='red', marker='o')\n",
    "\n",
    "#在绘图中添加水平网格\n",
    "ax1.yaxis.grid(True, linestyle='-', which='major', color='lightgrey',\n",
    "               alpha=0.5)\n",
    "\n",
    "ax1.set(\n",
    "    axisbelow=True,  \n",
    "    title='Comparison of IID Bootstrap Resampling Across Five Distributions',\n",
    "    xlabel='Distribution',\n",
    "    ylabel='Value'\n",
    ")\n",
    "\n",
    "box_colors = ['lightblue', 'pink']\n",
    "num_boxes = len(data)\n",
    "medians = np.empty(num_boxes)\n",
    "for i in range(num_boxes):\n",
    "    box = bp['boxes'][i]\n",
    "    box_x = []\n",
    "    box_y = []\n",
    "    for j in range(5):\n",
    "        box_x.append(box.get_xdata()[j])\n",
    "        box_y.append(box.get_ydata()[j])\n",
    "    box_coords = np.column_stack([box_x, box_y])\n",
    "\n",
    "    ax1.add_patch(Polygon(box_coords, facecolor=box_colors[i % 2]))\n",
    "    \n",
    "    med = bp['medians'][i]\n",
    "    median_x = []\n",
    "    median_y = []\n",
    "    for j in range(2):\n",
    "        median_x.append(med.get_xdata()[j])\n",
    "        median_y.append(med.get_ydata()[j])\n",
    "        ax1.plot(median_x, median_y, 'k')\n",
    "    medians[i] = median_y[0]\n",
    "#最后，以水平对齐方式过度绘制样本平均值\n",
    "#在每个盒子的中央\n",
    "    ax1.plot(np.average(med.get_xdata()), np.average(data[i]),\n",
    "             color='w', marker='o', markeredgecolor='k')\n",
    "\n",
    "# 设置轴范围和轴标签\n",
    "ax1.set_xlim(0.5, num_boxes + 0.5)\n",
    "top = 40\n",
    "bottom = -5\n",
    "ax1.set_ylim(bottom, top)\n",
    "ax1.set_xticklabels(np.repeat(random_dists, 2),\n",
    "                    rotation=45, fontsize=8)\n",
    "\n",
    "# Due to the Y-axis scale being different across samples, it can be\n",
    "# hard to compare differences in medians across the samples. Add upper\n",
    "# X-axis tick labels with the sample medians to aid in comparison\n",
    "# 两位小数精确\n",
    "pos = np.arange(num_boxes) + 1\n",
    "upper_labels = [str(round(s, 2)) for s in medians]\n",
    "weights = ['bold', 'semibold']\n",
    "for tick, label in zip(range(num_boxes), ax1.get_xticklabels()):\n",
    "    k = tick % 2\n",
    "    ax1.text(pos[tick], .95, upper_labels[tick],\n",
    "             transform=ax1.get_xaxis_transform(),\n",
    "             horizontalalignment='center', size='medium',\n",
    "             weight=weights[k], color=box_colors[k])\n",
    "        \n",
    "#add legend\n",
    "fig.text(0.80, 0.08, f'{N} Random Numbers',\n",
    "         backgroundcolor=box_colors[0], color='black', weight='roman',\n",
    "         size='medium')\n",
    "fig.text(0.80, 0.045, 'IID Bootstrap Resample',\n",
    "         backgroundcolor=box_colors[1],\n",
    "         color='black', weight='roman', size='medium')\n",
    "fig.text(0.80, 0.015, '*', color='black', backgroundcolor='silver',\n",
    "         weight='roman', size='medium')\n",
    "fig.text(0.815, 0.013, ' Average Value', color='black', weight='roman',\n",
    "         size='medium')\n",
    "\n",
    "plt.xlabel(\"我是x轴\", fontsize = 20)\n",
    "plt.ylabel(\"我是y轴\", fontsize = 20)\n",
    "plt.title('我是标题', fontsize = 20)\n",
    "plt.tick_params(labelsize=13)\n",
    "plt.grid()\n",
    "plt.savefig(\"O_07.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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